EV Charging Recommendations With Anonymous Sustainability Ranking
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Solution Overview
Problem
Current electric vehicle charging recommendation systems fail to provide customized and secure recommendations that accurately reflect individual priorities, leading to inefficient reduction of environmental impacts, as they do not adequately consider diverse charging parameters and prioritize user privacy, resulting in mistrust and decreased usage.
Innovation Solution
A system where an electric vehicle establishes a secure anonymous connection with a charging recommendation platform to transmit and receive customized battery charging parameters, enabling personalized recommendations based on environmental cost values and user priorities, promoting the use of renewable energy sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If charging recommendation systems use generic charging parameters, then system complexity is reduced, but the accuracy of environmental impact reduction is insufficient
Solution Approach 1:
The patent segments charging parameters into multiple categories including environmental priorities, charging preferences, vehicle specifications, and location constraints. This segmentation allows the system to handle complex parameters in manageable groups while maintaining high accuracy in environmental impact assessment without overwhelming system complexity
Solution Approach 2:
The patent introduces a new dimension of environmental cost values that quantify the environmental impact of different charging options. By adding this dimensional metric, the system transforms generic charging recommendations into personalized environmental optimization strategies, improving accuracy without proportionally increasing complexity
2Measurement precision
If charging recommendation systems collect detailed user data for personalized recommendations, then recommendation accuracy improves, but user privacy is compromised
Solution Approach 1:
The patent introduces an intermediary layer that processes charging parameters through environmental cost values without exposing raw user data. The system uses aggregated environmental cost metrics as intermediaries between user preferences and recommendation outputs, maintaining recommendation accuracy while protecting user privacy through data abstraction
Solution Approach 2:
The patent transforms sensitive user charging parameters into standardized environmental cost values with defined ranges and weights. By changing the parameter representation from detailed user data to standardized environmental metrics, the system maintains personalization capability while reducing privacy risks through parameter transformation
3Measurement precision
If charging stations implement comprehensive sustainability tracking, then environmental impact measurement improves, but infrastructure cost increases
Solution Approach 1:
The patent creates a universal environmental cost value framework that can be applied across diverse charging station types and energy sources. This universal system allows comprehensive environmental tracking using standardized metrics that work for solar, wind, grid, and hybrid charging stations, improving measurement precision without requiring station-specific infrastructure for each energy type
Solution Approach 2:
The patent enables charging stations to self-report their energy source composition and operational characteristics using standardized environmental cost parameters. This self-service approach allows comprehensive environmental tracking where stations provide their own data within the unified framework, reducing the need for external monitoring infrastructure and lowering overall system costs
Data Source
AI summary
Methods and systems are provided for transmitting a charging recommendation from a cloud-based server to an electric vehicle (EV), where the charging recommendation includes preferred options for charging stations and/or charging times. In one example, a method comprises receiving, at a cloud-based charging recommendation system of an EV charging system, a set of battery charging parameters from the EV via a secure anonymous connection; determining a set of candidate charging stations for the EV, based on the received battery charging parameters; estimating sustainability, cost, and preference metrics for each candidate charging station; ranking the future charge events based on the sustainability, cost, and preference metrics; selecting one or more charge options for the EV from the ranked future charge events; generating the charging recommendation with the one or more charge options; and transmitting the charging recommendation to the EV via the secure anonymous connection.